For most brain analysis studies based on fMRI data, inter-subject spatial alignment of fMRI data is a necessary precursor, and a better inter-subject spatial correspondence often leads to improved statistical analysis results with enhanced statistical significance. Inter-subject spatial alignment of fMRI data is typically achieved through registering their co-registered structural MRI images due to their relatively high spatial resolution and good image texture information. However, a good alignment of brain anatomical structures across different subjects does not necessarily lead to good inter-subject functional consistency in that functional units are not necessarily located relative to anatomical structures consistently due to functional variability across subjects. In order to improve the functional consistency across subjects, spatial smoothing of the functional image of each subject is commonly applied in practice after the structural MRI image based registration. However, the adverse effects of image smoothing, including functional signal blurring and loss of fine-grained information, will be brought into the subsequent group analysis. Hence, it is desired to develop an image registration method capable of achieving better functional consistency across subjects in fMRI studies.
Recently, several functional information based image registration methods have been proposed for achieving better consistency of brain functions across subjects. A cortical surface alignment method was proposed to maximize similarity of functional signals between subjects in Sabuncu et al, “Function-based intersubject alignment of human cortical anatomy”, Cerebral Cortex 20 (2010), pgs. 130-140. In this method, the Pearson correlations between inter-subject functional signals were maximized to register different subjects' cortex surface meshes based upon an assumption that functional signals are synchronic across different subjects. However, such an assumption is not necessarily true in most cases. In resting-state fMRI (rs-fMRI) images, for instance, even at the same position of same subject, no significant correlations exist between the functional signals scanned at different time. Thus, such a method is not reliable for rs-fMRI images. To overcome this drawback, methods have been proposed to achieve functional image registration by maximizing similarity of functional connectivity patterns at the same spatial locations between different subjects, i.e., using functional connectivity measures as features to drive the image registration. In the method proposed in Conroy et al, “fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity”, Advances in Neural Information Processing Systems (2009), pgs. 378-386, the whole-brain functional connectivity matrix was used as a descriptor of functional information on the cortical surface and cortical surface meshes of different subjects were registered by minimizing the Frobenius norm of difference of their functional connectivity matrices. However, the global functional connectivity pattern based functional image registration is not robust since the global functional connectivity patterns are sensitive to local perturbations. A small spatial rotation or shift of functional units may alter the global functional connectivity patterns dramatically, thus leads to misregistration. In addition, the dimension of the global connectivity matrix is often too large for the subsequent processing. In Langs et. al, “Functional Geometry Alignment and Localization of Brain Area”, Advances in Neural Information Processing Systems (2010), pgs. 1225-1133, features were first extracted from the whole-brain functional connectivity matrix using a spectral embedding technique, functional images were then aligned by a point set registration method in the feature space, and finally the transformation information was mapped back to the original fMRI image space for achieving image registration. A problem of the spectral embedding based feature extraction is that ad hoc techniques have to be utilized to make the extracted features of different subjects comparable since embedding is defined up to rotation, order, and sign of individual coordinate axes.